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Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology
Can Cui, Xindong Zheng, Ruining Deng +8
Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges. In digital pathology, ano…
IRS: Incremental Relationship-guided Segmentation for Digital Pathology
Ruining Deng, Junchao Zhu, Juming Xiong +14
Continual learning is rapidly emerging as a key focus in computer vision, aiming to develop AI systems capable of continuous improvement, thereby enhancing their value and practica…
Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology
Lining Yu, Mengmeng Yin, Ruining Deng +9
Segmenting glomerular intraglomerular tissue and lesions traditionally depends on detailed morphological evaluations by expert nephropathologists, a labor-intensive process suscept…
Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology
Junlin Guo, Siqi Lu, Can Cui +14
Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datas…
HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization
Yucheng Tang, Yufan He, Vishwesh Nath +11
In digital pathology, the traditional method for deep learning-based image segmentation typically involves a two-stage process: initially segmenting high-resolution whole slide ima…
GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model
Lining Yu, Mengmeng Yin, Ruining Deng +9
Moving from animal models to human applications in preclinical research encompasses a broad spectrum of disciplines in medical science. A fundamental element in the development of…
Dataset Distillation in Medical Imaging: A Feasibility Study
Muyang Li, Can Cui, Quan Liu +4
Data sharing in the medical image analysis field has potential yet remains underappreciated. The aim is often to share datasets efficiently with other sites to train models effecti…
Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +16
Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell s…
HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis
Ruining Deng, Quan Liu, Can Cui +10
Panoramic image segmentation in computational pathology presents a remarkable challenge due to the morphologically complex and variably scaled anatomy. For instance, the intricate…
PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
Ruining Deng, Quan Liu, Can Cui +11
Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney system comprises various…
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Ruining Deng, Can Cui, Lucas W. Remedios +12
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (M…